SUMMARY

Ontologist and Semantic Architect with 10+ years building RDF/OWL ontologies, knowledge graphs,

and semantic metadata systems for complex enterprise environments. Deep hands-on experience with

W3C standards (SPARQL, OWL, SKOS, RDFS), graph databases (GraphDB Ontotext, Neo4j), and

enterprise taxonomy management platforms (TOMS). Track record of enabling cross-system data

integration, semantic search, and personalization at scale across siloed enterprise content ecosystems.

Strong foundation in NLP-driven metadata extraction, information architecture, and content

management strategy. Proven ability to translate ambiguous business requirements into structured,

intuitive ontologies that drive analytics, discovery, and customer experience outcomes.

SKILLS

- Ontology Design & Modeling: RDF, OWL 2, RDFS, SKOS, Dublin Core, Schema.org
- Query & Reasoning: SPARQL 1.1, SHACL validation, OWL-DL inference
- Graph Databases: GraphDB Ontotext, Neo4j, Amazon Neptune, Stardog
- Taxonomy & TOMS: Enterprise taxonomy management platforms (TOMS), Protege
- NLP & Metadata Extraction: spaCy, NLTK, Hugging Face Transformers, entity extraction,

auto-tagging

- Content & Information Management: Elasticsearch, Apache Solr, content modeling,

DAM/CMS integration

- Programming: Python, JavaScript/Node.js, Java, SPARQL, Shell
- Cloud & Infrastructure: AWS (S3, Lambda, ECS), Docker, Kubernetes, CI/CD • Data &

Storage: PostgreSQL, MongoDB, Redis, PGVector, NoSQL document stores

PROFESSIONAL EXPERINCE

Ontology Engineer / Knowledge Graph Specialist

- Designed and maintained enterprise RDF/OWL ontologies for an AI product suite,

enabling semantic interoperability across computer vision, NLP, and agentic AI

modules.

- Built a knowledge graph layer using GraphDB Ontotext for a large-scale multi-

camera vision system,



mapping entity relationships across face recognition, inventory,

and warehouse management domains.

- Developed SPARQL-based analytics pipelines for cross-domain querying, reducing

prospect research time from hours to under two minutes in a presales orchestration

tool.

- Led semantic metadata extraction using spaCy and custom NER models to auto-tag

enterprise documents, improving content discoverability by 40%.• Created taxonomy structures and controlled vocabularies for a government AI

services empanelment initiative, ensuring standards compliance across consortium

deliverables.

- Applied SHACL validation constraints to enforce ontology quality and consistency

across distributed development teams.

- Mapped business requirements to ontological models for client-facing AI solutions

across retail, education, and quick-commerce verticals, translating ambiguous

stakeholder needs into structured semantic schemas.

Senior Developer / Semantic Integration Lead

- Led semantic layer design for a consumer super-app, building OWL ontologies to

unify product catalogs, user profiles, and transaction data across multiple brands

within a conglomerate.

- Implemented entity resolution and semantic deduplication using graph-based

matching algorithms, improving data consistency across distributed databases and

internal APIs.

- Developed SPARQL query interfaces for business intelligence dashboards, enabling

cross-brand analytics and personalization at scale.





- Built and deployed containerized semantic microservices (Docker/AWS ECS) for

real-time content classification and recommendation.

- Integrated LLM-based agentic workflows with knowledge graph backends for

intelligent customer support and internal tooling.

- Designed RDF-based metadata schemas for progressive web app content, enabling

semantic search and discovery across product categories.

Data Engineer / Ontology Specialist

- Built enterprise taxonomies and classification schemas for ETL data pipelines,

ensuring semantic consistency in data warehousing workflows.

- Developed RDF/SKOS vocabularies for content tagging in full-stack applications,

enabling faceted search and content discovery.

- Created ontology-driven data validation frameworks using Python, reducing data

quality issues by 35% in high-volume ingestion pipelines.

- Worked with enterprise vocabulary management tooling to maintain controlled

vocabularies across distributed teams.

- Contributed to data workbench backend optimizations, integrating semantic metadata

layers for cross-team data collaboration.

- Containerized semantic services and deployed on Kubernetes clusters for scalable

graph query processing.

Developer / Content Taxonomy Contributor

- Developed content classification taxonomies for video ad targeting, enabling

semantic audience segmentation using industry-standard video ad metadata formats.

- Built automated tagging pipelines using NLP techniques to extract semantic metadata

from video content for ad personalization.• Configured CI/CD pipelines and cloud-based infrastructure for content delivery with

semantic content routing.

CERTIFICATION & TRAINING

- Neo4j Graph Database - Certified Qualified
- AWS Solutions Architect - Associate

📌 Semantic Architect | Knowledge Graph Engineer - Senior Ontologist (Delhi)
🏢 Codemythos solutions
📍 Delhi